Improving medical/biological data classification performance by wavelet preprocessing Export

@proceedings{citeulike:1305162, abstract = {Many real-world datasets contain noise which could degrade the performances of learning algorithms. Motivated from the success of wavelet denoising techniques in image data, we explore a general solution to alleviate the effect of noisy data by wavelet preprocessing for medical/biological data classification. Our experiments are divided into two categories: one is of different classification algorithms on a specific database, and the other is of a specific classification algorithm (decision tree) on different databases. The experiment results show that the wavelet denoising of noisy data is able to improve the accuracies of those classification methods, if the localities of the attributes are strong enough.}, author = {Li, Qi and Li, Tao and Zhu, Shenghuo and Kambhamettu, C.}, citeulike-article-id = {1305162}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1184022}, journal = {Data Mining, 2002. ICDM 2002. Proceedings. 2002 IEEE International Conference on}, keywords = {data-classification, denoising}, pages = {657--660}, posted-at = {2008-05-06 22:37:26}, priority = {2}, title = {Improving medical/biological data classification performance by wavelet preprocessing}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1184022}, year = {2002} }

TY - CONF ID - citeulike:1305162 L3 - citeulike-article-id:1305162 N2 - Many real-world datasets contain noise which could degrade the performances of learning algorithms. Motivated from the success of wavelet denoising techniques in image data, we explore a general solution to alleviate the effect of noisy data by wavelet preprocessing for medical/biological data classification. Our experiments are divided into two categories: one is of different classification algorithms on a specific database, and the other is of a specific classification algorithm (decision tree) on different databases. The experiment results show that the wavelet denoising of noisy data is able to improve the accuracies of those classification methods, if the localities of the attributes are strong enough. JF - Data Mining, 2002. ICDM 2002. Proceedings. 2002 IEEE International Conference on EP - 660 TI - Improving medical/biological data classification performance by wavelet preprocessing SP - 657 KW - data-classification KW - denoising AU - Li, Qi AU - Li, Tao AU - Zhu, Shenghuo AU - Kambhamettu, C PY - 2002/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1184022 ER -